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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Unexpected trends in the enhanced Ce 3+ surface concentration in ceria–zirconia catalyst materials

Despite the immense importance of ceria–zirconia solid solutions in heterogeneous catalysis, and the growing consensus that catalytic activity correlates with the concentration of reduced Ce 3+ species and accompanying oxygen vacancies, the extent of reduction at the surfaces of these materials, where catalysis occurs, is unknown. Using angle-resolved X-ray Absorption Near Edge Spectroscopy (XANES), we quantify under technologically relevant conditions the Ce 3+ concentration in the surface (2–3 nm) and bulk regions of ceria–zirconia films grown on single crystal yttria-stabilized zirconia, YSZ (001). In all circumstances, we observe substantial Ce 3+ enrichment at the surface relative to the bulk. Surprisingly, the degree of enhancement is highest in the absence of Zr. This behavior stands in direct contrast to that of the bulk in which the Ce 3+ concentration monotonically increases with increasing Zr content. Overall, these results suggest that while Zr enhances the oxygen storage capacity in ceria, undoped ceria may have higher surface catalytic activity. They further urge caution in the use of bulk properties as surrogate descriptors for surface characteristics and hence catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The electron-transfer intermediates of the oxygen evolution reaction (OER) as polarons by in situ spectroscopy

The conversion of diffusive forms of energy (electrical and light) into short, compact chemical bonds by catalytic reactions regularly involves moving a carrier from an environment that favors delocalization to one that favors localization. While delocalization lowers the energy of the carrier through its kinetic energy, localization creates a polarization around the carrier that traps it in a potential energy minimum. The trapped carrier and its local distortion—termed a polaron in solids—can play a role as a highly reactive intermediate within energy-storing catalytic reactions but is rarely discussed as such. Here, we present this perspective of the polaron as a catalytic intermediate through recent in situ and time-resolved spectroscopic investigations of photo-triggered electrochemical reactions at material surfaces. The focus is on hole-trapping at metal–oxygen bonds, denoted M–OH*, in the context of the oxygen evolution reaction (OER) from water. The potential energy surface for the hole-polaron defines the structural distortions from the periodic lattice and the resulting “active” site of catalysis. This perspective will highlight how current and future time-resolved, multi-modal probes can use spectroscopic signatures of M–OH* polarons to obtain kinetic and structural information on the individual reaction steps of OER. A particular motivation is to provide the background needed for eventually relating this information to relevant catalytic descriptors by free energies. Lastly, the formation of the O–O chemical bond from the consumption of M–OH*, required to release O 2 and store energy in H 2 , will be discussed as the next target for experimental investigations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Density functional theory studies of transition metal carbides and nitrides as electrocatalysts

Transition metal carbides and nitrides are interesting non-precious materials that have been shown to replace or reduce the loading of precious metals for catalyzing several important electrochemical reactions. The purpose of this review is to summarize density functional theory (DFT) studies, describe reaction pathways, identify activity and selectivity descriptors, and present a future outlook in designing carbide and nitride catalysts for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), nitrogen reduction reaction (N 2 RR), CO 2 reduction reaction (CO 2 RR) and alcohol oxidation reactions. Furthermore, this topic is of high interest to scientific communities working in the field of electrocatalysis and this review should provide theoretical guidance for the rational design of improved carbide and nitride electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fast Na diffusion and anharmonic phonon dynamics in superionic Na 3 PS 4

The design of new solid electrolytes (SEs) hinges on identifying and tuning relevant descriptors. Phonons describe the atomic dynamics in crystalline materials and provide a basis to encode possible minimum energy pathways for ion migration, but anharmonic effects can be large in SEs. Identifying and controlling the pertinent phonon modes coupled most strongly with ionic conductivity, and assessing the role of anharmonicity, could therefore pave the way for discovering and designing new SEs via phonon engineering. In this work, we investigate phonons in Na 3 PS 4 and their coupling to fast Na diffusion, using a combination of neutron scattering, ab initio molecular dynamics (AIMD), and extended molecular dynamics based on machine-learned potentials. We identify that anharmonic soft-modes at the Brillouin zone boundary of the anharmonically stabilized cubic phase constitute key phonon modes that control the Na diffusion process in Na 3 PS 4 . We demonstrate how these strongly anharmonic phonon modes enable Na-ions to hop along the minimum energy pathways. Further, the quasi-elastic neutron scattering (QENS) measurements, supplemented with ab initio and machine-learned molecular dynamics simulations, probe the Na diffusivity and diffusion mechanism. These results offer detailed microscopic insights into the dynamic mechanism of fast Na diffusion and provide an avenue to search for further Na solid electrolytes.

42 ENGINEERING↗
DOE OSTI2022

Data-driven design of novel halide perovskite alloys

The great tunability of the properties of halide perovskites presents new opportunities for optoelectronic applications as well as significant challenges associated with exploring combinatorial chemical spaces. Here, in this work, we develop a framework powered by high-throughput computations and machine learning for the design and prediction of mixed cation halide perovskite alloys. In a chemical space of ABX 3 perovskites with a selected set of options for A, B, and X species, pseudo-cubic structures with B-site mixing are simulated using density functional theory (DFT) and several properties are computed, including stability, lattice constant, band gap, vacancy formation energy, refractive index, and optical absorption spectrum, using both semi-local and hybrid functionals. Neural networks (NN) are used to train predictive models for every property using tabulated elemental properties of A, B, and X site species as descriptors. Starting from a DFT dataset of 229 points, we use the trained NN models to predict the structural, energetic, electronic, and optical properties of an extensive dataset of 17955 compounds, and perform high-throughput screening in terms of stability, band gap, and defect tolerance, to obtain 392 promising compounds that are ranked as potential absorbers according to their photovoltaic figure of merit. Compositional trends in the screened set of attractive mixed cation halide perovskites are revealed and additional computations are performed on selected compounds. The data-driven design framework developed here is promising for designing novel mixed compositions and can be extended to a wider perovskite chemical space in terms of A, B, and X species, different kinds of mixing at the A, B, or X sites, non-cubic phases, and other properties of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2022

[Fe 4 S 4 ] cubane in sulfite reductases: new insights into bonding properties and reactivity

The dissimilatory sulfite reductase enzyme has very characteristic active site where the substrate binds to an iron site, ligated by a siroheme macrocycle and a thiol directly connected to a [Fe 4 S 4 ] cluster. This arrangement gives the enzyme remarkable efficiency in reducing sulfite and nitrite all the way to hydrogen sulfide and ammonia. For the first time we present a theoretical study where substrate binding modalities and activation are elucidated using active site models containing proton supply side chains and the [Fe 4 S 4 ] cluster. Density functional theory (DFT) was deployed in conjunction with the energy decomposition scheme (as implemented in AMS), the quantum theory of atoms in molecules (QTAIM), and conceptual DFT (cDFT) descriptors. We quantified the role of the electrostatic interactions inside the active site created by the side chains as well as the influence of the [Fe4S4] cluster on the substrate binding. Furthermore, using conceptual DFT results we shed light of the activation process, thus, laying foundation for further mechanistic studies. We found that the bonding of the ligands to the iron complex is dominated by electrostatic interactions, but the presence of the [Fe 4 S 4 ] cubane leads to substantial changes in electronic interaction. The spin state of the cubane, however, affects the binding energy only marginally. The conceptual DFT results show that the presence of the [Fe 4 S 4 ] cubane affects the reactivity of the active site as it is involved in electron transfer. This is corroborated by an increase in the electrophilicity index, thus making the active site more prone to react with the ligands. The interaction energies between the ligand and the siroheme group are also increased upon the presence of the cubane group, thus, suggesting that the siroheme group is not an innocent spectator but plays an active role in the reactivity of the dSIR active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The impact of G-quadruplex dynamics on inter-tetrad electronic couplings: a hybrid computational study

The G-quadruplex is a fascinating nucleic acid motif with implications in biology, medicine, and nanotechnologies. G-quadruplexes can form in the telomeres at the edges of chromosomes and in other guanine-rich regions of the genome. They can also be engineered for exploitation as biological materials for nanodevices. Their higher stiffness and higher charge transfer rates make them better candidates in nanodevices than duplex DNA. For the development of molecular nanowires, it is important to optimize electron transport along the wire axis. One powerful basis to do so is by manipulating the structure, based on known effects that structural changes have on electron transport. Here, for this work, we investigate such effects, by a combination of classical simulations of the structure and dynamics and quantum calculations of electronic couplings. We find that this structure–function relationship is complex. A single helix shape parameter alone does not embody such complexity, but rather a combination of distances and angles between stacked bases influences charge transfer efficiency. By analyzing linear combinations of shape descriptors for different topologies, we identify the structural features that most affect charge transfer efficiency. We discuss the transferability of the proposed model and the limiting effects of inherent flexibility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present Catlas, a framework that distributes and automates the generation of adsorbate-surface configurations and ML inference of DFT energies to achieve this goal. Catlas is open source, making ML assisted catalyst screenings easy and available to all. To demonstrate its efficacy, we use Catlas to explore catalyst candidates for the direct conversion of syngas to multi-carbon oxygenates. For this case study, we explore 947 stable/metastable binary, transition metal intermetallics as possible catalyst candidates. On this subset of materials, we are able to predict the adsorption energy of key descriptors, *CO and *OH, with good accuracy (0.16, 0.14 eV MAE, respectively). Using the projected selectivity towards C 2+ oxygenates from an existing microkinetic model, we identified 144 candidate materials. For 10 promising candidates, DFT calculations reveal a good correlation with our assessment using ML. Finally, among the top elemental combinations were Pt–Ti, Pd–V, Ni–Nb, and Ti–Zn, all of which appear unexplored experimentally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ligand removal energetics control CO 2 electroreduction selectivity on atomically precise, ligated alloy nanoclusters

Atomically precise, thiolate-protected gold nanoclusters (TPNCs) exhibit remarkable catalytic performance for the electrochemical reduction of carbon dioxide (CO 2 R) to CO. The origin of their high CO 2 R activity and selectivity has been attributed to partial ligand removal from the thiolate-covered surfaces of TPNCs to expose catalytically active sulfur atoms. Recently, heterometal doped (alloy) TPNCs have been shown to exhibit enhanced CO 2 R activity and selectivity compared to their monometallic counterparts. However, systematic studies on the effect of doping (metal type and location on TPNC) on active site exposure and CO 2 R activity are missing in literature. Herein, we apply Density Functional Theory calculations to investigate the effect of heterometal (Pt, Pd, Hg and Cd) doping of Au 25 (SR) 18 TPNC on the active site exposure and CO 2 R activity and selectivity. In this study, we reveal that doping significantly modifies relevant TPNC electronic properties, such as electron affinity, while also altering partial ligand removal and carboxyl (*COOH) intermediate formation energies. Furthermore, we demonstrate that changing the dopant (e.g. Hg) position can change the selectivity of the TPNC towards CO (g) or H 2(g) formation, highlighting the importance of dopant locations in TPNC-based CO 2 R. Most notably, we report a universal (i.e. capturing different dopant types and positions) linear trend between the ligand removal energy and i) the *COOH formation energy, as well as, ii) the hydrogen (*H) formation energy on the different alloy TPNCs. Thus, utilizing the ligand removal energy as a descriptor for CO 2 RR activity and selectivity, our work opens new avenues for accelerated computational screening of different alloy TPNCs for electrocatalytic CO 2 R applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships. Here, this review provides recent progress in the ML-aided rational design of heterogeneous catalysts with the focus on SACs in terms of structure–activity relationships, feature importance analysis, high-throughput screening, stability, and metal–support interactions for electrochemistry. Support vector machine (SVM), random forest regression (RFR), and deep neural networks (DNN) along with atomic properties are mainly used for the design of SACs. The ML results have shown that the number of electrons in the d orbital, oxide formation enthalpy, ionization energy, Bader charge, d-band center, and enthalpy of vaporization are mainly the most important parameters for the defining of the structure–activity relationships for electrochemistry. However, the black-box nature of ML techniques occasionally makes a physical interpretation of descriptors, such as the Bader charge, d-band center, and enthalpy of vaporization, non-trivial. At the current stage, ML application is limited by the lack of a large and high-quality database. Future prospects for the development of a large database and a generalized ML algorithm for SAC design are discussed to give insights for further studies in this field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Screening binary alloys for electrochemical CO 2 reduction towards multi-carbon products

Electrochemical reduction of CO 2 (eCO 2 R) to high-value chemicals presents an attractive approach for utilizing CO 2 . Copper (Cu) is presently the only electrocatalyst that fulfills this purpose with notable activity, but selectivity remains a problem. To identify catalysts for eCO 2 R with high selectivity towards multicarbon (C 2(+) ) products, we explore binary systems composed of strongly and weakly CO binding metals alloyed with Cu, Fe, Co, Ni, and Pd. A total number of 142 alloys with two commonly studied configurations, L1 2 and L1 0 , are simulated with density functional theory (DFT). We leverage recent progress in the atomistic understanding of the eCO 2 R mechanism and use the binding energies of CO* and C* as descriptors when screening for C 2(+) selectivity. We evaluate the stability of the binary alloys by analyzing the formation energy of the clean alloy surfaces. Our theoretical screening identifies about 16 Cu-based alloys and 18 non-Cu based alloys with optimal C 2(+) selective properties for eCO 2 R. For the non-Cu based binary alloys, the p-block elements play an important role in tuning the C* and CO* adsorption energies. In terms of stability, most of the Cu-based systems alloyed with metals that exhibit strong CO* binding are unstable. Ni-based alloys are more stable than the Co-based alloys followed by the Fe-based alloys, and all the Pd-based alloys are stable. In general, the L1 0 structural Fe, Co, Ni, and Pd-based alloys are more stable than the corresponding L1 2 alloys. Our approach identifies materials known to have good C 2(+) selectivity, but it also proposes several other promising materials that have not previously been tested for eCO 2 R.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes

Solid-state batteries are projected to exhibit improved energy densities and safety compared to liquid-electrolyte-based systems. Consequently, the development of solid electrolytes that can conduct cations at rates comparable to that of liquid electrolytes is an active area of research. To facilitate this search it is desirable to understand the chemical and structural features that control ion transport through a crystalline lattice. Here, machine learning (ML) is used to identify features that influence ion mobility in solids, quantify the relative importance of those features, and demonstrate how the variation of individual features alters ion mobility. Adopting the anti-perovskite lattice as a model system, ML algorithms were trained to predict migration barriers from a data set containing hundreds of barriers calculated using density functional theory. In this study, more than 10 6 feature sets of non-redundant descriptors were evaluated for their ability to predict migration barriers. The present analysis correctly reiterates the importance of several features that are known to influence mobility – for example, anion polarizability – but also identifies others whose importance is not widely recognized. Lattice properties such as hopping distance and channel width exhibit the greatest influence on cation mobility. Individual conditional expectation analyses performed on these features shows that barriers decrease as hopping distance decreases and as channel width increases. The defect formation energy – which is commonly associated with the concentration of defects, but not with their mobility – is identified as an important feature for interstitial migration. In sum, this analysis aids in the design of optimal solid electrolytes by simplifying the multi-dimensional design space to a sub-set of properties that are the most important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Crystal engineering of heterocyclic arylene(ethynylene) oligomers through programmed aromatic stacking

Conjugated oligomers and polymers consisting of aromatic heterocycles are examples of next-generation organic electronic materials. However, control and optimization of the self-assembly of these materials in the solid-state, which is a crucial parameter for high efficiency performance, remains an unsolved challenge in materials development. In this work, we provide valuable insight into aromatic stacking interactions between fluorinated arene rings (ArF) and heterocycles with different electronic character (Het), and elaborate on the prospect of using these interactions to control the solid-state configurations of three-ring phenyleneethynylene oligomers (PEs). Oligomers possessing heterocycles typically thought of as electron-rich (ER) show blue-shifted optical spectra in the solid-state, while those incorporating heterocycles typically thought of us electron deficient (ED) show red-shifted solid-state optical spectra. Crystal structures show ArF-Het stacking interactions between the fluorinated side-chains and ER units, which twist the backbone out of planarity and prevent chromophore aggregation. Further, the interactions are absent in ED oligomers, highlighting cofacial incompatibility between ArF rings and ED units. A combination of TD-DFT computations and novel heterocycle descriptors reinforce our assignment of the oligomers as ED or ER, while exhaustive conformer analysis shows ArF-Het stacking interactions are a significant contributor to compound stability. Overall, this work describes the importance of heterocycle electronics in conformational control in the solid state, understanding of which can be a valuable asset in the development of novel optoelectronic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Proton transfer kinetics of transition metal hydride complexes and implications for fuel-forming reactions

Proton transfer reactions involving transition metal hydride complexes are prevalent in a number of catalytic fuel-forming reactions, where the proton transfer kinetics to or from the metal center can have significant impacts on the efficiency, selectivity, and stability associated with the catalytic cycle. Here, this review correlates the often slow proton transfer rate constants of transition metal hydride complexes to their electronic and structural descriptors and provides perspective on how to exploit these parameters to control proton transfer kinetics to and from the metal center. A toolbox of techniques for experimental determination of proton transfer rate constants is discussed, and case studies where proton transfer rate constant determination informs fuel-forming reactions are highlighted. Opportunities for extending proton transfer kinetic measurements to additional systems are presented, and the importance of synergizing the thermodynamics and kinetics of proton transfer involving transition metal hydride complexes is emphasized.

08 HYDROGEN↗